Bibliographic record
Abstract
The urban ‘south’ is built through practices that are necessarily multiple. Here, culture is conceptually foundational. Not simply context, it is a field that allows for productive participation in urban space; as such, it works as a device to activate citizenship in the city symbolically and pragmatically. In this chapter, I draw on the practice of hip hop in Dakar, Senegal, to explore these complex processes and debates. In doing so, I distance myself from previous work on hip hop based on race ( Forman and Neal 2004 ; Neate 2004 ; Rose 1994 ) and/or age ( Kitwana 2002 ; Watkins 2005 ). Such a racial explanation for hip hop is a geo-historical framing of hip hop as a Black American culture, which can be confronted in its forms elsewhere with the actual experiences of Latinos living in American ghettoes, of Portuguese or Maghreb immigrant descendants stigmatized in French banlieues , of Algonkin Natives parked in Canadian reserves; or, in this case, even young Africans marginalized in gerontocratic societies such as Dakar. Drawing on Chang (2005) , I also question the definition of hip hop as a contemporary youth culture. In fact, ‘generations are fictions’; they are ‘used in larger struggle over power’ and stand as ‘a way of imposing a narrative’ (ibid.: 1). In reality, hip hop pioneers in the USA, France or Senegal, who still actively participate in this movement, are a variety of ages: in their forties, and in some instances, over fifty! 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".